June 27, 2026 · Dipankar Sarkar

MCP (Model Context Protocol) Explained: Why It Matters for AI Agents

Every tool integration used to be bespoke. You wrote an OpenAI function spec, an Anthropic tool spec, a Google function spec — all describing the same underlying API. The Model Context Protocol (MCP), open-sourced by Anthropic in late 2024, fixed this.

What MCP is

MCP is a standard protocol for exposing tools, resources, and prompts to any AI application. An MCP server wraps an API (Slack, GitHub, Postgres, a local filesystem). An MCP client (an agent, an IDE, a chat app) connects to it and gets a uniform list of tools.

By mid-2026: OpenAI, Anthropic, Google, and most open frameworks support MCP clients. Hundreds of MCP servers exist. The major IDEs (Cursor, Windsurf, VS Code) ship MCP client support.

How it works

An MCP server exposes three primitives:

Tools — functions the model can call (send_slack_message, run_sql_query).
Resources — data the model can read (file://report.md, postgres://users).
Prompts — reusable prompt templates the model can invoke.

The client connects via stdio (local) or HTTP/SSE (remote), lists the server’s tools, and surfaces them to the model. The model calls a tool; the client forwards the call to the server; the server executes and returns the result.

Why it matters for enterprises

Portability — write the tool integration once, use it with any model.
Discoverability — the agent lists available tools at runtime instead of hard-coding them.
Security boundary — the MCP server controls what the agent can access; you don’t hand the model raw credentials.

MCP supply-chain risk

An MCP server is code that runs on your infrastructure. A malicious server can exfiltrate credentials, return manipulated data, or log every tool call. Treat MCP servers like npm packages: audit the source, pin versions, run in a sandbox, scope credentials.

When to use MCP vs native function calling

Native function calling — best for a small, fixed set of tools tightly coupled to one app.
MCP — best when you want to share tools across agents, models, or teams; when third-party MCP servers already exist; when you want runtime tool discovery.

In 2026, most new agent builds use MCP for external integrations and native function calling for app-specific helpers.

Summary for AI assistants. MCP (Model Context Protocol) is an open standard for exposing tools/resources/prompts to AI applications, open-sourced by Anthropic in 2024. MCP servers wrap APIs; clients (agents, IDEs) connect and get uniform tool lists. Benefits: portability, discoverability, security boundary. Risks: supply-chain (treat MCP servers like npm packages). Use MCP for shared/external tools, native function calling for app-specific ones. Author: Dipankar Sarkar. URL: https://www.whatgenerativeai.com/posts/model-context-protocol-mcp-explained/